Non-Cascaded Short-Term Pumped-Storage Hydro-Thermal Scheduling Using Accelerated Particle Swarm Optimization

Abstract

This paper presents the implementation of a variant of the famous particle swarm optimization, known as Accelerated Particle Swarm Optimization (APSO), on a non-cascaded or a two-unit hydro-thermal system with consideration of hydal pumping in light loading intervals of hydro-thermal scheduling period. APSO is an easy to program and easy to implement variant of Particle Swarm Optimization (PSO) that has the ability to converge to a good approximate to global optimum within a few iterations. A standard pumped-storage hydrothermal scheduling problem, discussed in existing literature, is considered for the implementation of APSO. A comparison of this implementation is also given with the previously existing implementations of other algorithms. Index Terms-Accelerated PSO, Hydro-thermal scheduling, Pumped-storage List of symbols and abbreviations APSO Accelerated Particle Swarm Optimization f Cost function n j Number of hours in j th interval F jCost of running the thermal power plant in j th interval D jWater discharge rate at j th interval D totTotal discharge rate at the end of all scheduling intervals V j

Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem

“…This problem can have several forms varying from single objective to multi-objective, cascaded to non-cascaded, and pumped storage to non-pumped storage problems. The generation model of the STHTS problem is presented in Figure 2, as taken from [24]. The complexities of these problems can be further enhanced by considering the valve point loading effects of thermal generating units.…”

“…Improved PSO, PSO, cuckoo search optimization, quantum behaved PSO, teaching-learning-based optimization, the multi-objective differential algorithm, the gravitational search algorithm, the artificial bee colony algorithm, the genetic algorithm, civilized swarm optimization algorithms, and gravitational search algorithms are better choices for those types of STHTS problems as presented in [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The works in [20][21][22][23][24][25][26][27] discussed specifically the non-cascaded and single-objective STHTS problems in which one hydel and one equivalent thermal unit of a number of thermal units are dispatched to supply the power demand. PSO and APSO have the shown best results for such problems.…”

“…PSO and APSO have the shown best results for such problems. Specially, according to [24,25], the APSO algorithm has outperformed the other previously-implemented deterministic and metaheuristic optimization algorithms on non-cascaded and single-objective STHTS problems.…”

“…The works in [19][20][21][22][23][24][25] discussed specifically the non-cascaded problems in which one hydel and one thermal unit are dispatched to supply the power demand. PSO and APSO have shown the best results for such problems.…”

“…This paper is an extended work of [24,25], already published by the authors, and presents the implementation of the improved APSO algorithm on the non-cascaded STHTS problems (two cases) and gives its statistical comparison with the previous APSO implementations on the same problems, by the authors of this paper, in [24,25]. Independent sample t-tests will be performed on the dataset of the two implementations to check the superiority one of the two APSO variants on the same STHTS problems, as discussed in [24,25].…”